1. Field of the Invention
The present invention relates to a controlling apparatus for use with controlling, recognizing, diagnosing processes, and so forth for an object, a method thereof, and a medium having a program for accomplishing the method thereof.
2. Description of the Related Art
Conventionally, to control a multilayered neural network, error back propagation method has been used.
In the error back propagation method, with respect to data (p.sup.n, t.sup.n) (where n=1, 2, . . . , N; N is the number of sets) that is a set of an input signal p.sup.n =(p.sub.1.sup.n, p.sub.2.sup.n, . . . p.sub.m1.sup.n) (where m1 is the number of dimensions) and a teacher signal t.sup.n =(t.sub.1.sup.n, t.sub.n.sup.2, . . . , t.sub.m3.sup.n) (where m3 is the number of dimensions), the input signal p.sup.n is calculated in the forward direction and an output signal o.sup.n =(o.sub.1.sup.n, o.sub.2.sup.n, . . . , o.sub.m3.sup.n) is obtained. The square sum of an error between the output signal o.sup.n and the teacher signal t.sup.n is obtained and multiplied by 1/2. The result is defined as an error function of an n-th data set, E.sup.n. The average of all data sets is defined as an error function E. ##EQU1##
The connection weight of the multilayered neural network is repeatedly corrected in such a manner that the error function E becomes minimum. When the value of the error function E decreases to a predetermined value, the correction of the connection weight of the multilayered neural network is stopped.
For example, the following problems can be input to the multilayered neural network.
Problem 1) The ranges of errors between teacher signals and output signals have been designated. Errors between teacher signals and output signals of individual units in the output layer should be converged to predetermined ranges.
Problem 2) When an error between teacher signal and output signal of one of units in the output layer is converged to a predetermined range, errors between teacher signals and output signals of the other units can be deviated from predetermined ranges.
Problem 3) When ranges of errors between teacher signals and output signals of individual units in the output layer have been designated to a predetermined range, the errors between teacher signals and output signals should be converged to the predetermined range.
As described above, in the conventional controlling method, an error function for equally dealing with errors between teacher signals and output signals of units in the output layer is evaluated. When the value of the error function decreases to a predetermined value, the correction of the connection weight of the multilayered neural network is stopped.
Thus, with respect to the problem 1, if the ranges of the errors between teacher signals and output signals of units in the output layer are largely different, although an error between teacher signal and output signal of a unit with a small error range has not been converted to a predetermined range, the correction of the connection weight of the multilayered neural network may be stopped. In the case that the designated value of the error function is small and errors between teacher signals and output signals of all the units are converged to predetermined ranges, since the connection weight of the multilayered neural network is repeatedly corrected until the errors of output signals and teacher signals of unit with large ranges become small, the number of times of the correcting process increases.
With respect to the problem 2, when the error between the output signal and the teacher signal of a particular unit is sufficiently small, although the error between teacher signal and output signal of the unit has been converged to the predetermined range, since errors between teacher signals and output signals of the other units do not have predetermined respective ranges, the stop condition of the correction of the connection weight is not satisfied. Thus, the number of times of the correcting process increases. In addition, since the connection weight is corrected so as to converge the error between teacher signal and output signal of a unit to a predetermined range, the connection weight of the units continuously varies. Thus, an error between output signal and teacher signal of a unit with a small error between an output signal and a teacher signal is not converged to a predetermined range.
With respect to the problem 3, since the error between the output signal and the teacher signal of each unit in the output layer does not always constantly vary, there are two types of units of which errors between output signals and teacher signals of some units sharply vary, whereas errors of other units do not almost vary. In other words, since errors between output signals and teacher signals vary in individual units, it is difficult to converge errors between output signals and teacher signals of units in the output layer to predetermined ranges.
As technologies that deal with the above-described problems, the following related art references are known.
1) Japanese Patent Laid-Open Application No. 4-30280
In this related art reference, the absolute value of an error E between output data O of each output node in the output layer of the neural network and teacher data T corresponding to the output data O is compared with a threshold value a. If the absolute value of the error E is smaller than the threshold value a, the value of the error E of the output node is reset to "0". When the absolute value of the error E is larger than the threshold value a, the value of the error E of the output node is used as it is. Thus, the connection weight can be prevented from being unnecessarily corrected due to an output node with a small value of the error E.
2) Japanese Patent Laid-Open Application 4-360291
In this related art reference, an error between a output y.sub.k .degree. of each output node in the output layer of the neural network and a similarity (teacher data) y'.sub.k is multiplied by a term that varies corresponding to the similarity (the term is the similarity or the square thereof). The result is defined as an error function E. The connection weight of the neural network is corrected in such a manner that the error function E becomes minimum. In other words, as the coefficient of the error function, the similarity or the square thereof is used. In this system, when the similarity is large, the evaluation of error between the output y.sub.k .degree. and the similarity y'.sub.k is increased. When the similarity is small, the evaluation of error is decreased. Thus, the amount of variation of the connection weight of the neural network corresponding to an output signal with a large similarity is increased. The amount of variation of the connection weight of the neural network corresponding to an output signal with a small similarity is decreased.
The present invention is made from another point of view of the above-described related art references. An object of the present invention is to provide a controlling apparatus for executing an adaptive controlling process.
Another object of the present invention is to provide a controlling method for executing an adaptive controlling process.
A further object of the present invention is to provide a medium having a program for accomplishing a controlling method for executing an adaptive controlling process.